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Record W3010803670 · doi:10.1063/1.5140010

Direct thermal emission testing of aperiodic dielectric stack for narrowband thermal emission at mid-IR

2020· article· en· W3010803670 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Applied Physics · 2020
Typearticle
Languageen
FieldEngineering
TopicThermal Radiation and Cooling Technologies
Canadian institutionsCarleton University
Fundersnot available
KeywordsEmissivityStack (abstract data type)DielectricMaterials scienceAperiodic graphPermittivityNarrowbandSiliconOptoelectronicsSilicon dioxideThermalOpticsComposite materialPhysics

Abstract

fetched live from OpenAlex

Direct thermal testing of aperiodic all-dielectric structures is presented, and its high-Q and emissivity properties are experimentally demonstrated for carbon dioxide (CO2) gas sensing applications. Using a 7-layer dielectric stack consisting of alternating layers of silicon (Si) and silicon dioxide (SiO2), backed by a metallic ground plane, an emissivity of 0.7 and a Q-factor of 113 are achieved at 70°C. Although this structure was already proposed in the literature, this is the first time direct thermal testing is reported, thereby showing narrowband emission properties of such structures when heated above room temperatures. An all-dielectric stack is thus found to be a simple, deposition-based structure that does not require any lateral mask preparation as frequency selectivity is achieved using an aperiodic arrangement of alternating dielectrics with contrasting permittivity. Superior performance over the periodically stacked structure is also demonstrated using numerical examples.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.219
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it